Methods and apparatus for computing zonal flow rates in reservoir wells

ABSTRACT

A method for determining at least one zonal flow rate of a reservoir well comprises: receiving input data representing at least one state parameter of the reservoir well; determining a generative model of the at least one state parameter and a generative model of the at least one zonal flow rate; iteratively adapting the generative models to minimize divergence from available data until the generative models and the available data converge; and, once the generative models and the available data converge, using the generative model of the at least one zonal flow rate to compute the at least one zonal flow rate.

BACKGROUND

The present invention relates generally to the electrical, electronicand computer arts, and, more particularly, to techniques for computingzonal flow rates in reservoir wells.

The accurate real-time estimation of zonal flow in reservoir wellsremains a challenge to date. The ability to monitor flow in suchsettings is of significance in reservoir operations, such as, forexample, production operation and management, preventative maintenancescheduling and reservoir characterization improvement. Some of thespecific challenges associated with flow monitoring include, but are notlimited to, identification of the contribution of different productivezones to the total flow measured at the well head, description ofproduction behavior for each individual zone, and timely detection offlow trends and changes.

BRIEF SUMMARY

Principles of the invention, in accordance with embodiments thereof,provide techniques for determining at least one zonal flow rate of areservoir well. In one aspect, a method for determining at least onezonal flow rate of a reservoir well comprises: receiving input datarepresenting at least one state parameter of the reservoir well;determining a generative model of the at least one state parameter and agenerative model of the at least one zonal flow rate; iterativelyadapting the generative models to minimize divergence from availabledata until the generative models and the available data converge; and,once the generative models and the available data converge, using thegenerative model of the at least one zonal flow rate to compute the atleast one zonal flow rate.

In accordance with another embodiment of the invention, an apparatusincludes a memory and at least one processor coupled to the memory. Theprocessor is operative: to determine at least one zonal flow rate of areservoir well comprises: to receive input data representing at leastone state parameter of the reservoir well; to determine a generativemodel of the at least one state parameter and a generative model of theat least one zonal flow rate; to iteratively adapt the generative modelsto minimize divergence from available data until the generative modelsand the available data converge; and, once the generative models and theavailable data converge, to use the generative model of the at least onezonal flow rate to compute the at least one zonal flow rate.

As used herein, “facilitating” an action includes performing the action,making the action easier, helping to carry the action out, or causingthe action to be performed. Thus, by way of example and not limitation,instructions executing on one processor might facilitate an actioncarried out by instructions executing on a remote processor, by sendingappropriate data or commands to cause or aid the action to be performed.For the avoidance of doubt, where an actor facilitates an action byother than performing the action, the action is nevertheless performedby some entity or combination of entities.

One or more embodiments of the invention or elements thereof can beimplemented in the form of a computer program product including acomputer readable storage medium with computer usable program code forperforming the method steps indicated. Furthermore, one or moreembodiments of the invention or elements thereof can be implemented inthe form of a system (or apparatus) including a memory, and at least oneprocessor coupled to the memory and operative to perform exemplarymethod steps. Yet further, in another aspect, one or more embodiments ofthe invention or elements thereof can be implemented in the form ofmeans for carrying out one or more of the method steps described herein;the means can include (i) hardware module(s), (ii) software module(s)stored in a computer readable storage medium (or multiple such media)and implemented on a hardware processor, or (iii) a combination of (i)and (ii); any of (i)-(iii) implement the specific techniques set forthherein.

These and other features and advantages of the present invention willbecome apparent from the following detailed description of illustrativeembodiments thereof, which is to be read in connection with theaccompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The following drawings are presented by way of example only and withoutlimitation, wherein like reference numerals (when used) indicatecorresponding elements throughout the several views, and wherein:

FIG. 1 depicts an exemplary reservoir well that may be useful inimplementing one or more aspects and/or elements of the invention;

FIG. 2 is a flow diagram depicting at least a portion of an exemplarymethod for monitoring zonal flow rates, according to an embodiment ofthe invention; and

FIG. 3 depicts a computer system that may be useful in implementing oneor more aspects and/or elements of the invention.

It is to be appreciated that elements in the figures are illustrated forsimplicity and clarity. Common but well-understood elements that may beuseful or necessary in a commercially feasible embodiment may not beshown in order to facilitate a less hindered view of the illustratedembodiments.

DETAILED DESCRIPTION

Embodiments of the present invention will be described herein in thecontext of illustrative methods and apparatus for monitoring zonal flowsin reservoir wells. It is to be appreciated, however, that the inventionis not limited to the specific apparatus and/or methods illustrativelyshown and described herein. Moreover, it will become apparent to thoseskilled in the art given the teachings herein that numerousmodifications can be made to the embodiments shown that are within thescope of the claimed invention. Thus, no limitations with respect to theembodiments shown and described herein are intended or should beinferred.

FIG. 1 depicts an exemplary reservoir well that may be useful inimplementing one or more aspects and/or elements of the invention.Specifically, FIG. 1 depicts an exemplary reservoir well having amultiple zone single string completion 100. As is known by one skilledin the art, such a reservoir well allows for extraction of a resource(which may include, for example, one or more liquids such as petroleumand/or gases such as propane) from a plurality of productive zones,which may be located at different depths, for example. In oneembodiment, the reservoir well is used to extract a multiphase flowincluding a mixture of oil, gas, and water. Well 100 includesperforation intervals at each of three productive zones 110, 120, 130.The number of productive zones is purely exemplary, as is the choice ofwell completion. One skilled in the art would understand that aspects ofthe invention can be applied to other well completions, such as multiplestring completions.

Each of the productive zones has associated therewith an isolationpacker 115, 125, 135. In this exemplary embodiment, these packers areHydro-6 hydraulic-set retrievable packers commercially available fromSchlumberger. Sliding sleeves are positioned between each packer. Thus,sliding sleeve 121 is located between packers 115 and 125, and slidingsleeve 131 is located between packers 125 and 135. Although thecombination of isolation packers and sliding sleeves can be configuredto allow for selective isolation of the productive zones, it is oftenpreferable to produce a comingled flow to allow for simultaneousextraction from all of the productive zones.

Safety joints, also known as blast joints, are also positioned betweenthe packers to reduce the risk of erosion damage to the tubing string.Thus, joint 122 is located between packers 115 and 125, and joint 126 islocated between packers 125 and 135. Tubing 116, 123, 127, 132 may alsobe installed between the aforementioned components where doing so isnecessary or desirable.

The top portion of well 100 (i.e., above packer 115) includes a pair offlow couplings 111 and 113 with a safety valve 112 therebetween. Theflow couplings reduce the risk of erosion damage to the tubing string,while the safety valve facilitates emergency containment. The bottomportion of well 100 (i.e., below packer 135) includes pup joint 131,no-go profile nipple 132, and re-entry guide 133. Pup joint 131 may beoptionally inserted to adjust the length of the string. The no-goprofile nipple 132 provides an installation point for sensors, and alsoprevents dropped tools from falling out of the end of the tubing. There-entry guide 133 allows easy re-entry into the tubing of wellintervention tool strings, such as wireline or coiled tubing.

Although not shown in FIG. 1, fiber optic cable (FOC) may be attached toeither the outside of the inner casing over the full length or connectedto the outside of the production tubing. Thus, an exemplary embodimentmay implement fiber optics distributed acoustic sensing (DAS) usingsound signals derived from fiber optic cable aligned along well casing.Optical pulses of light enter a fiber optic cable and very small levelsof reflected light (back scatter) within the fiber are detected andinterpreted. Any acoustic event along the entire fiber length createssound waves in the ground which cause micro-changes in the shape of thefiber which disturbs the back scatter. An acoustic signal is thenrebuilt from the change in the back scatter data. In addition to helpingto monitor production from different perforation intervals and initiatetimely remedial action in accordance with aspects of the presentinvention further discussed herein, fiber optics DAS can also monitorfracture growth in reservoirs.

Exemplary embodiments of well 100 may be operative to generate acontinuous stream of data, including PVT (pressure, volume, andtemperature) analysis of fluids. For example, well 100 may have bottomhole pressure (BHP) and bottom hole temperature (BHT) gauges installedat every perforation interval, e.g., productive zones 110, 120, 130.There may be temperature, pressure and/or flow rate gauges at thewellhead. Well 100 may implement distributing temperature sensing (DTS),distributed pressure sensing (DPS), and/or distributed acoustic sensing(DAS). Such systems advantageously allow detection, classification, andactivity along the entire length of the FOC. For example, there may bepressure, temperature, and/or acoustic sensor readings every meter (orevery few meters). There may also be strain measurements everycentimeter (or every few centimeters).

In addition to the aforementioned sensory data, well parameters may alsobe used by embodiments of the present invention. Well parameters mayinclude well geometry, such as geometry of pipe and orifice, expansioncoefficients of the pipe and orifice, and/or geometry of pressure taps.Other well parameters may include dynamic viscosity, density at processconditions, porosity, saturation, permeability, and/or relativepermeability; these parameters may be measured using sonic, gamma ray,neutron porosity, and/or resistivity techniques.

Despite the large volume and richness of well data signals (e.g., DTS,DPS, and DAS data, as well as well logs and well parameters), accuratereal time monitoring of zonal flow rates (e.g., the flow rate of oil,water and/or gas at each productive zone 110, 120, 130 at specified timeintervals) remains problematic. Monitoring of zonal flow rates may alsoinclude identification of the contribution of the different productivezones to the total flow measured at the well head, description ofproduction behavior (e.g., decline curve) of each individual zone, andtimely detection of flow trend and changes.

However, several factors contribute to the complex nature of highfidelity zonal flow rate inference, including:

Ill-posedness—only limited (spatially) number of indirect boundarymeasurements are taken, entailing infinite number of states that equallymatch the data

Model mis-specification—physical models (particularly of multi-physicsproblems) host a broad range of inadequacies, from formulation, throughnumerical estimation to stability

Process conditions—imperfect knowledge of the process conditions (e.g.errors in viscosity or density estimation), especially at large depthsof the reservoir, inherently propagates as uncertainty in flow rate

Rapid, voluminous data—data is of high volume and rate, requiringcomputationally efficient infrastructure for its processing

Exemplary embodiments of the present invention advantageously addressthe aforementioned shortcomings of the prior art by minimizing theamount of input data required for accurate zonal flow rate monitoring,as well as handling model mis-specification and recovering model errors.Thus, embodiments allow for real time analysis of streaming well data toaccurately estimate zonal flow rates.

Exemplary embodiments provide numerous technical advantages relative tothe prior art. For example, embodiments of the present inventionfacilitate reservoir operations such as production optimization andmanagement, preventative maintenance scheduling, and improved reservoircharacterization. Embodiments of the present invention may also: monitorand optimize production performance, predict zonal flow rates in thefuture using predictive analytics, schedule preventive maintenanceaccurately, provide real time continuous reservoir surveillance,optimize reservoir management, prioritize production operations andoperational response, and/or provide early warning for deterioratingconditions.

Thus, embodiments of the present invention can improve the productivity,safety, and characterization of reservoirs. For example, embodiments ofthe present invention may allow well operators to optimize recovery byaccurately monitoring production from various contributing productionzones, as well as by accurately monitoring fracture growth and behavior.Embodiments may also advantageously facilitate optimization ofwork-over, stimulation, fracturing, and/or acidizing jobs, for example,allowing for more effective work-overs and more efficient stimulationjobs. Embodiments may also allow for increased safety through earlydetection and prediction of anomalous situations, such as abnormalmicroseismic activity. Thus, embodiments may allow for optimized use ofassets and consumables, including optimized reservoir targeting.Embodiments may also produce a significant reduction in unplanneddeferment and improved production rates from wells.

Embodiments of the present invention involve an inversion framework, andmore particularly a novel bi-level non-convex non-linear optimizationformulation for accurate inversion. Embodiments of the present inventioninclude a joint inversion with model mis-specification handling whichmitigates ill-posedness and uncertainty through structure exploitationof a sparse representation of state and zonal fluid flows, followed byformulation of a stochastic optimization approach for low rank recovery.Embodiments may also include dictionary learning for better posed-nessof the problem, resulting in a higher quality and geologicallyconsistent solution. Such embodiments may also include tuning andupdating of the dictionary, as well as other interactive optimizationwith user feedback and controls.

The inventors have found that the salient dimension of the underlyingflow behavior (and its state space) resides in a far lower dimensionthan expected by conventional means of inference. Embodiments of thepresent invention advantageously leverage this finding for: (1) Modelreduction—for situations where the fidelity of the model is exceedingthe desired one and efficient computation is essential (e.g. ProperOrthogonal Decomposition, Discrete Empirical Interpolation Method); and(2) Uncertainty mitigation—through introduction of a universal governingprinciple/a-priori assumption, such as simplicity. Leveraging thesesimplicity assumptions (e.g. sparse representation, nuclear/atomic norm,manifolds, etc) advantageously provides superior fidelity and accuracyof the flow inference problem.

With the above in mind, it may be helpful to formally define the problemof finding a high fidelity estimate of the flow rate q. Let m be themodel parameters (e.g. geometry, permeability, porosity) that mayinvolve uncertainty. The state parameters (pressure P, temperature T,viscosity μ, density ρ) are defined by the fields u≡{p,T,μ,ρ}. Thecontrols (pressure p_(c), rate q_(c)) are denoted by c≡{p_(c),q_(c)}.Let g be a simulation model that links controls c and model parameters mwith the state u, where model mis-specification error is represented byη:u=g(c;m)+η.

The observation operator F links the state u with the observablesd≡{p^(obs),T^(obs),μ^(obs),ρ^(obs)} along with measurement noise ∈, asfollows: d=F(u)+∈. Zonal flow q is related to state parameters, andmodel mis-specification error ζ:q=h(u,m,c)+ζ.

Assume the state fields u≡{p,T,μ,ρ} representation can be determinedsparsely using a sparsity generative model: u=

₁(D₁,α) where D₁ is a dictionary (or any other space spanning object)and α is a sparse vector and a function of time. Because state ucomprises several types of parameters, D₁ can be a heterogeneousdictionary

$\quad\begin{pmatrix}D_{p} & \; & \; & \; \\\; & D_{T} & \; & \; \\\; & \; & D_{\mu} & \; \\\; & \; & \; & D_{\rho}\end{pmatrix}$and the sparse vector α could be

$\begin{pmatrix}\alpha_{p} \\\alpha_{T} \\\alpha_{\mu} \\\alpha_{\rho}\end{pmatrix}.$

Assume that the zonal flow q can be represented sparsely using thegenerative model: q=

₂(D₂,λ) where D₂ is a dictionary (or any other space spanning object)and λ is a sparse vector and a function of time. In a bi-leveloptimization framework, the optimization problem for sparse recovery ofthe state, zonal flows and model can be formulated as follows:

min 1 ⁢ ( F ⁡ ( 1 ⁢ ( D 1 , α ) ; m ) , d ) + 2 ⁢ ( ∑ ⁢ 2 ⁢ ( D 2 , λ ) , qtotal obs ⁡ ( m ) ) + β ⁢ ⁢ ( α , λ ) s . t .  α  0 ≤ τ 1  λ  0 ≤ τ 2 1⁢( 1 ⁢ ( D 1 , α ) , ℊ ⁡ ( c , m ) ) ≤ f 1 ⁡ ( η ^ ) 2 ⁢ ( 2 ⁢ ( D 2 , λ ) , h⁡( u , c , m ) ) ≤ f 2 ⁡ ( ζ ^ ) η ^ = min η ⁢  η ⁡ ( α )  * Ϛ ^ = min Ϛ ⁢ Ϛ ⁡ ( λ )  *where P₁, P₂ are noise models for state parameter estimation and zonalflow estimation respectively; R is a regularization function consideringboth α and λ; T₁, T₂ are cardinality thresholds to ensure sparsereconstruction of state vector (u) and zonal flow vector (q); q_(total)^(obs) is measured total flow at the surface (wellhead) which mayinclude the respective flows of oil, gas and water at the surface; L₁,L₂ are loss functions suitably chosen to represent deviation fromobserved values; f₁, f₂ are model mis-specification influence models forstate parameter estimation and zonal flow estimation respectively;

₁,

₂ are generative models for state parameter estimation and zonal flowestimation respectively and ∥.∥※:=Σσ_(i) represents the nuclear normoperator, which is the sum of the singular values of the operand.

An embodiment may optionally include various techniques forincorporating structural characteristics (which may include constraintsand/or limitations) for flow q and/or state u. These characteristics canbe explicit, such as: (a) Vorticity—for close to laminar flow,vortices/turbidity is less likely and can be penalized, e.g ∥∇×u∥; (b)Energy ∥Gu∥₂ ²; and/or (c) Continuity

${{\frac{\partial{f(u)}}{\partial t} - {\bigtriangledown \cdot {{\mathcal{g}}\left( {f(u)} \right)}}}}.$

These characteristics can also be implicit instead, such as: (a)Sparsity—signals/state can be represented by a small number of elements∥α∥1; (b) Low rank—the rank of the representation/spanning set is small∥θ∥*; and/or (c) Operator splitting—the state can be represented as acomposite of low rank & sparse represented signal (or any other normmixture):

${\min\limits_{\alpha,\theta}{\alpha }_{1}} + {\gamma{\theta }_{*}}$s.t.  u = α + θ

An embodiment may also incorporate a goal-oriented inference approach,in which goal-oriented modeling informs goal-oriented inference whichinforms a decision. The dimension of the desired inferred entities (flowrates) is far lower than that of the model state. Thus, an illustrativeembodiment incorporating a goal-oriented inference approach may avoidexplicit recovery of an intermediate high dimensional state and insteadlink directly the desired inferred entity and the data, therebyachieving a high-quality inversion in reduced time.

Thus, embodiments of the present invention involve an advanced inversionframework for estimation of zonal flow rate using data from reservoirs.A sparse modeling based bi-level interactive optimization formulationsolution may involve iteratively solving convergence, minimizing thedivergence with respect to available flow and distributed sensing data,and recovering the most accurate model, using sparse representation ofstate parameters and the desired estimate (flow rate). State and flowrate estimation may be performed by non-linear inversion with modelfidelity constraints. The model may use mis-specification recovery toresolve errors in physical modeling using provided data.

More particularly, embodiments of the present invention involve abi-level optimization framework that performs joint non-linear inversionwith model mis-specification handling. The bi-level optimizationframework can be solved using techniques such as stochastic optimizationwith low rank recovery: within the solution, the uncertainty in zonalflow rates and state parameters is quantified and also reduced todesirable limits using interactive user feedback. A goal-orientedinference approach may be used in which focusing on inference of desiredvariables leads to faster convergence. For example, goal-directedinversion formulation may be utilized so that data and optimizations arefocused on zonal flow rate inference.

FIG. 2 is a flow diagram depicting at least a portion of an exemplarymethod for monitoring zonal flow rates, according to an embodiment ofthe invention. The method begins with the receipt of input data 210,which may be structured and/or unstructured. Receipt of input data 210may include receipt of well data 202 from reservoir well 201, which maybe real time streaming sensory (e.g., DTS, DPS, and/or DAS) data and/orproduction (e.g., oil, gas, water) data. Reservoir well 201 may, forexample, be similar to reservoir well 100 discussed above with referenceto FIG. 1. Receipt of input data 210 may additionally or alternativelyinclude receipt of external data 212 from external data source(s) 211.External data 212 may include, for example, a reservoir well model(which may include well geometry and other static parameters of thewell) and/or other relatively static (e.g., batch mode rather thanstreaming) data.

Step 220 involves sparsity based inversion, as discussed above. Step 220may include inference of zonal flow rates with adaptive prototypegenerative models. The sparsity based inversion 220 may involvebidirectional communication (e.g., interaction) with a calibratedreservoir simulator 225, which in turn has bidirectional communication(e.g., interaction) with a calibrated well simulator 235, that can beused to determine effects of potential changes to parameters ofreservoir well 201 without actually implementing these (untested andpossibly deleterious) changes on reservoir well 201. Step 230 involvesmodel mis-specification handling, also as discussed above, to resolveerrors in physical modeling using provided data, and more particularlythrough a hybrid physics-based and data-driven approach. The modelmis-specification handling 230 may also involve bidirectionalcommunication (e.g., interaction) with the reservoir simulator 225,which in turn has bidirectional communication (e.g., interaction) withthe well simulator 235.

Step 240 tests to see whether convergence is achieved. If step 240determines that convergence is not achieved (241), the method proceedsto step 250. Step 250 involves interactive optimization in which a usercan provide feedback and/or make changes 251, 252 with respect toparameters associated with sparsity based inversion step 220 and/ormodel mis-specification handling step 230. Examples of parameters whichmay be interactively optimized by a user in step 250 include: dictionarytuning and/or update (e.g., D₁ and/or D₂); regularization functions(e.g., R); physical model selection and/or tuning (e.g., m); and/orsparsity constraints (e.g., T₁,T₂). After step 250, the method returnsto step 220, as indicated by the solid arrow 251 rather than the dottedarrow 252.

If step 240 determines that convergence is achieved (242), the methodproceeds to step 260, in which the zonal flow rates are output. This mayinvolve, for example, communicating the rates to a user (e.g., on alocal display and/or via an electronic message) and/or storing the rates(e.g., in a data store) for future reference. Step 270 includesclassification and prescriptive analytics based at least in part on thezonal flow rates output in step 260. For example, as discussed above,the zonal flow rates can be used to determine adjustments 272 whichshould be made to operational parameters associated with well 201 inorder to optimize its performance, and/or to detect potential hazardoussituations where a production flow is already damaged or will getdamaged in the near future.

Steps 210, 220, 230, 240, 250, 260 and/or 270 may be implemented local(e.g., on-site real time analytic processing (RTAP)) or remote (e.g., anon-shore real time operations center (RTOC)) to reservoir well 201. Inone embodiment, substantially similar input data is received (e.g., 210)by both an on-site RTAP and an off-site RTOC. The on-site RTAP usesin-motion analytics (e.g., InfoSphere Streams software commerciallyavailable from International Business Machines (IBM)) to generateultra-low latency results in step 260 and to make direct operationaladjustments to the well in step 270. The off-site RTOC uses traditionalanalytics (e.g., a data warehouse/Hadoop) to generate more detailedresults and reports in step 260 and to make longer term fielddevelopment adjustments in step 270. In order to support large scaledata handling and high performance analytics, a preferred embodimentimplements at least a portion of these steps using software commerciallyavailable from IBM, such as Big Insights and/or other components of theBig Data platform, and/or hardware commercially available from IBM, suchas high-performance computer clusters and/or servers.

Given the discussion thus far, it will be appreciated that, in generalterms, an exemplary method for determining at least one zonal flow rateof a reservoir well according to an aspect of the invention comprises:receiving input data representing at least one state parameter of thereservoir well; determining a generative model of the at least one stateparameter and a generative model of the at least one zonal flow rate;iteratively adapting the generative models to minimize divergence fromavailable data until the generative models and the available dataconverge; and, once the generative models and the available dataconverge, using the generative model of the at least one zonal flow rateto compute the at least one zonal flow rate.

Exemplary System and Article of Manufacture Details

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

One or more embodiments of the invention, or elements thereof, can beimplemented in the form of an apparatus including a memory and at leastone processor that is coupled to the memory and operative to performexemplary method steps.

One or more embodiments can make use of software running on a generalpurpose computer or workstation. With reference to FIG. 3, such animplementation might employ, for example, a processor 302, a memory 304,and an input/output interface formed, for example, by a display 306 anda keyboard 308. The term “processor” as used herein is intended toinclude any processing device, such as, for example, one that includes aCPU (central processing unit) and/or other forms of processingcircuitry. Further, the term “processor” may refer to more than oneindividual processor. The term “memory” is intended to include memoryassociated with a processor or CPU, such as, for example, RAM (randomaccess memory), ROM (read only memory), a fixed memory device (forexample, hard drive), a removable memory device (for example, diskette),a flash memory and the like. In addition, the phrase “input/outputinterface” as used herein, is intended to include, for example, one ormore mechanisms for inputting data to the processing unit (for example,mouse), and one or more mechanisms for providing results associated withthe processing unit (for example, printer). The processor 302, memory304, and input/output interface such as display 306 and keyboard 308 canbe interconnected, for example, via bus 310 as part of a data processingunit 312. Suitable interconnections, for example via bus 310, can alsobe provided to a network interface 314, such as a network card, whichcan be provided to interface with a computer network, and to a mediainterface 316, such as a diskette or CD-ROM drive, which can be providedto interface with media 318.

Accordingly, computer software including instructions or code forperforming the methodologies of the invention, as described herein, maybe stored in one or more of the associated memory devices (for example,ROM, fixed or removable memory) and, when ready to be utilized, loadedin part or in whole (for example, into RAM) and implemented by a CPU.Such software could include, but is not limited to, firmware, residentsoftware, microcode, and the like.

A data processing system suitable for storing and/or executing programcode will include at least one processor 302 coupled directly orindirectly to memory elements 304 through a system bus 310. The memoryelements can include local memory employed during actual implementationof the program code, bulk storage, and cache memories which providetemporary storage of at least some program code in order to reduce thenumber of times code must be retrieved from bulk storage duringimplementation.

Input/output or I/O devices (including but not limited to keyboards 308,displays 306, pointing devices, and the like) can be coupled to thesystem either directly (such as via bus 310) or through intervening I/Ocontrollers (omitted for clarity).

Network adapters such as network interface 314 may also be coupled tothe system to enable the data processing system to become coupled toother data processing systems or remote printers or storage devicesthrough intervening private or public networks. Modems, cable modem andEthernet cards are just a few of the currently available types ofnetwork adapters.

As used herein, including the claims, a “server” includes a physicaldata processing system (for example, system 312 as shown in FIG. 3)running a server program. It will be understood that such a physicalserver may or may not include a display and keyboard.

As noted, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon. Anycombination of one or more computer readable medium(s) may be utilized.The computer readable medium may be a computer readable signal medium ora computer readable storage medium. A computer readable storage mediummay be, for example, but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,or device, or any suitable combination of the foregoing. Media block 418is a non-limiting example. More specific examples (a non-exhaustivelist) of the computer readable storage medium would include thefollowing: an electrical connection having one or more wires, a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), an optical fiber, a portable compact disc read-onlymemory (CD-ROM), an optical storage device, a magnetic storage device,or any suitable combination of the foregoing. In the context of thisdocument, a computer readable storage medium may be any tangible mediumthat can contain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It should be noted that any of the methods described herein can includean additional step of providing a system comprising distinct softwaremodules embodied on a computer readable storage medium; the modules caninclude, for example, any or all of the elements depicted in the blockdiagrams and/or described herein; by way of example and not limitation,a reservoir simulation module (e.g., consistent with the reservoirsimulator 225 in FIG. 2) and a calibrated well simulation module (e.g.,consistent with the calibrated well simulator 235 in FIG. 2). The methodsteps can then be carried out using the distinct software modules and/orsub-modules of the system, as described above, executing on one or morehardware processors 302. Further, a computer program product can includea computer-readable storage medium with code adapted to be implementedto carry out one or more method steps described herein, including theprovision of the system with the distinct software modules.

In any case, it should be understood that the components illustratedherein may be implemented in various forms of hardware, software, orcombinations thereof; for example, application specific integratedcircuit(s) (ASICS), functional circuitry, one or more appropriatelyprogrammed general purpose digital computers with associated memory, andthe like. Given the teachings of the invention provided herein, one ofordinary skill in the related art will be able to contemplate otherimplementations of the components of the invention.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method for determining and implementing anoperational adjustment to a reservoir well, the method comprising:receiving input data representing at least one state parameter of thereservoir well; determining a generative model of the at least one stateparameter and a generative model of the at least one zonal flow rate;iteratively adapting the generative models to minimize divergence fromavailable data until the generative models and the available dataconverge; once the generative models and the available data converge,using the generative model of the at least one zonal flow rate tocompute the at least one zonal flow rate; based on the computed at leastone zonal flow rate, determining the operational adjustment for thereservoir well; and communicating and implementing the operationaladjustment to the reservoir well; wherein at least one of determiningthe generative models and iteratively adapting the generative modelscomprises a joint non-convex non-linear inversion with goal-directedinversion formulation focused on inference of the at least one zonalflow rate.
 2. The method of claim 1, wherein the generative model of theat least one state parameter comprises a sparse representation of the atleast one state parameter and wherein the generative model of the atleast one zonal flow rate comprises a sparse representation of the atleast one zonal flow rate.
 3. The method of claim 1, wherein iterativelyadapting the generative models comprises interactive optimization inwhich at least one dictionary upon which at least one of the generativemodels is based is updated at least in part based on user feedback. 4.The method of claim 1, wherein the joint non-convex non-linear inversionfurther comprises a model mis-specification handling which utilizes ahybrid physics-based and data-driven approach.
 5. The method of claim 1,wherein the joint non-linear inversion comprises formulating a bi-leveloptimization framework.
 6. The method of claim 5, wherein the at leastone of determining the generative models and iteratively adapting thegenerative models further comprises solving the bi-level optimizationframework using stochastic optimization with low rank recovery.
 7. Themethod of claim 1, wherein iteratively adapting the generative modelscomprises: quantifying uncertainty of at least one of the at least onestate parameter and the at least one zonal flow rate; and reducing saiduncertainty to a specified limit using interactive user feedback.
 8. Themethod of claim 1, wherein the input data comprises distributed acousticsensing (DAS) data.
 9. The method of claim 8, wherein at least a portionof the DAS data represents back scatter within a fiber optic cableattached to the reservoir well.
 10. The method of claim 1, wherein theinput data comprises a well log from the reservoir well and a well modelfrom another data source.
 11. The method of claim 1, wherein: the atleast one state parameter comprises a plurality of state parameters;each of the input data, the available data, and the generative model ofthe at least one state parameter is representative of each of theplurality of state parameters; and the plurality of state parameterscomprises pressure, temperature, viscosity, and density.
 12. The methodof claim 1, wherein the reservoir well comprises a plurality ofperforated productive zones isolated by packers, and wherein the atleast one zonal flow rate represents a relative contribution of at leasta given one of the plurality of zones to a multiphase flow at a head ofthe well.
 13. The method of claim 12, further comprising determining adecline curve for at least the given one of the plurality of zones. 14.The method of claim 1, further comprising performing analytics based atleast in part on the computed at least one zonal flow rate, theanalytics comprising at least one of predictive analytics andprescriptive analytics.
 15. The method of claim 14, wherein theanalytics comprises increasing safety through early detection ofabnormal micro seismic activity.
 16. The method of claim 14, wherein theanalytics comprises monitoring fracture growth and behavior to optimizerecovery from the reservoir well.
 17. The method of claim 1, wherein atleast one of determining the generative models and iteratively adaptingthe generative models comprises use of a reservoir simulator inbidirectional communication with a well simulator.
 18. The method ofclaim 1, wherein during the joint non-convex non-linear inversion, thegenerative models are subject to respective sparsity constraints. 19.The method of claim 18, wherein iteratively adapting the generativemodels comprises interactive optimization in which at least one of thesparsity constraints is updated at least in part based on user feedback.20. The method of claim 18, wherein the respective sparsity constraintscomprise respective cardinality thresholds ensuring sparsereconstruction of respective vectors representing the at least one stateparameter and the at least one zonal flow rate.
 21. The method of claim1, wherein iteratively adapting the generative models comprisesinteractive optimization in which at least one of a regularizationfunction and a physical model is updated at least in part based on userfeedback.
 22. The method of claim 1, wherein at least one of determiningthe generative models and iteratively adapting the generative modelscomprises simplifying at least one of the models using at least one ofproper orthogonal decomposition and discrete empirical interpolation.23. An apparatus for determining and implementing an operationaladjustment to a reservoir well comprising: a memory; and at least oneprocessor coupled with the memory and configured: to receive input datarepresenting at least one state parameter of the reservoir well; todetermine a generative model of the at least one state parameter and agenerative model of the at least one zonal flow rate; to iterativelyadapt the generative models to minimize divergence from available datauntil the generative models and the available data converge; and oncethe generative models and the available data converge, to use thegenerative model of the at least one zonal flow rate to compute the atleast one zonal flow rate; based on the computed at least one zonal flowrate, to determine the operational adjustment for the reservoir well;and to communicate and to implement the operational adjustment to thereservoir well: wherein at least one of determining the generativemodels and iteratively adapting the generative models comprises a jointnon-convex non-linear inversion with goal-directed inversion formulationfocused on inference of the at least one zonal flow rate.
 24. An articleof manufacture comprising a computer program product for determining andimplementing an operational adjustment to a reservoir well the computerprogram product comprising a non-transitory machine-readable storagemedium having machine-readable program code embodied therewith, saidmachine-readable program code comprising: machine-readable program codeconfigured: to receive input data representing at least one stateparameter of the reservoir well; to determine a generative model of theat least one state parameter and a generative model of the at least onezonal flow rate; to iteratively adapt the generative models to minimizedivergence from available data until the generative models and theavailable data converge; and once the generative models and theavailable data converge, to use the generative model of the at least onezonal flow rate to compute the at least one zonal flow rate; based onthe computed at least one zonal flow rate, to determine the operationaladjustment for the reservoir well; and to communicate and to implementthe operational adjustment to the reservoir well; wherein at least oneof determining the generative models and iteratively adapting thegenerative models comprises a joint non-convex non-linear inversion withgoal-directed inversion formulation focused on inference of the at leastone zonal flow rate.